Detection and characterization of novel sequence insertions using paired-end next-generation sequencing. Academic Article uri icon

Overview

MeSH

  • Databases, Genetic
  • Genetic Variation
  • Genome, Human
  • Humans
  • Models, Genetic
  • Mutagenesis, Insertional
  • Polymorphism, Single Nucleotide

MeSH Major

  • Genomics
  • Sequence Analysis, DNA

abstract

  • In the past few years, human genome structural variation discovery has enjoyed increased attention from the genomics research community. Many studies were published to characterize short insertions, deletions, duplications and inversions, and associate copy number variants (CNVs) with disease. Detection of new sequence insertions requires sequence data, however, the 'detectable' sequence length with read-pair analysis is limited by the insert size. Thus, longer sequence insertions that contribute to our genetic makeup are not extensively researched. We present NovelSeq: a computational framework to discover the content and location of long novel sequence insertions using paired-end sequencing data generated by the next-generation sequencing platforms. Our framework can be built as part of a general sequence analysis pipeline to discover multiple types of genetic variation (SNPs, structural variation, etc.), thus it requires significantly less-computational resources than de novo sequence assembly. We apply our methods to detect novel sequence insertions in the genome of an anonymous donor and validate our results by comparing with the insertions discovered in the same genome using various sources of sequence data. The implementation of the NovelSeq pipeline is available at http://compbio.cs.sfu.ca/strvar.htm eee@gs.washington.edu; cenk@cs.sfu.ca

publication date

  • May 15, 2010

has subject area

  • Databases, Genetic
  • Genetic Variation
  • Genome, Human
  • Genomics
  • Humans
  • Models, Genetic
  • Mutagenesis, Insertional
  • Polymorphism, Single Nucleotide
  • Sequence Analysis, DNA

Research

keywords

  • Journal Article

Identity

Language

  • eng

PubMed Central ID

  • PMC2865866

Digital Object Identifier (DOI)

  • 10.1093/bioinformatics/btq152

PubMed ID

  • 20385726

Additional Document Info

start page

  • 1277

end page

  • 1283

volume

  • 26

number

  • 10